AI/ML Engineer - LLM Training & AI Coding Evaluator

We are looking for experienced AI/ML Engineers to join our team for projects focused on training, evaluating, and improving Large Language Models (LLMs) and AI coding agents. The role involves working with advanced AI models and evaluating their ability to solve real-world machine learning, AI engineering, and software development tasks. You will review AI-generated solutions, identify technical issues, and provide high-quality assessments that help improve model performance. What You'll Do - Work on challenging AI/ML and software engineering tasks generated for LLM evaluation and training. - Use modern AI coding assistants and frontier models to solve, test, and assess technical problems. - Review and evaluate AI-generated code related to machine learning, deep learning, LLMs, data pipelines, model training, inference, and MLOps. - Identify bugs, incorrect assumptions, security issues, edge cases, performance bottlenecks, and other weaknesses in generated solutions. - Compare solutions produced by different AI models and determine which solution is technically stronger and why. - Assess code quality, architecture, scalability, maintainability, and adherence to engineering best practices. - Validate whether AI-generated implementations actually work by running, testing, and debugging the code when required. - Provide clear technical reasoning and feedback that can be used to improve AI models. - Work with realistic, production-oriented AI/ML engineering scenarios rather than only theoretical problems. Requirements - 2+ years of professional experience in Machine Learning, AI Engineering, Data Science, Software Engineering, or a closely related field. - Strong Python programming skills. - Hands-on experience with machine learning or deep learning frameworks such as PyTorch, TensorFlow, Scikit-learn, or similar. - Good understanding of LLMs, generative AI, NLP, model training, inference, or AI applications. - Ability to understand and critically review complex technical implementations. - Experience building, debugging, or deploying real-world ML/AI systems. - Strong ability to explain why a solution is correct or incorrect, rather than simply identifying that something does not work. - Familiarity with AI coding tools such as Cursor, Claude Code, Codex, Windsurf, Gemini CLI, or similar tools is highly desirable. - Strong written English and ability to communicate technical concepts clearly. Nice to Have - Experience with LLM fine-tuning, RLHF/RLAIF, model evaluation, synthetic data generation, or LLM training. - Experience with ML infrastructure, MLOps, model serving, distributed training, or cloud platforms. - Experience working with AI coding agents or evaluating LLM-generated code. - Experience contributing to open-source ML/AI projects. - Experience working on LLM benchmarking, data annotation, preference ranking, or AI model evaluation projects. What We Are Looking For We are particularly interested in engineers who enjoy solving difficult technical problems and questioning whether an AI-generated solution is actually correct. You don't need to be a researcher working on foundation models. Strong hands-on ML/AI engineers who can understand production code, identify subtle technical problems, and provide thoughtful evaluations are a great fit. This is not a basic data annotation or content moderation role. The work requires genuine technical expertise in AI/ML and software engineering. How to Apply Please include the following in your proposal: 1. A brief summary of your AI/ML engineering experience. 2. Examples of ML, LLM, or AI systems you have built or worked on. 3. Which AI coding agents/tools you regularly use. 4. Your experience with LLM training, fine-tuning, evaluation, or AI model testing, if applicable. 5. Your hourly rate and availability. We may start with a short paid technical evaluation to assess your ability to solve and review AI/ML engineering tasks.